Development of region processing algorithm for HSTAMIDS: status and field test results

Ngan, Peter; Burke, Sean; Cresci, Roger; Wilson, Joseph N.; Gader, Paul D.; Ho, K. C.; Duvoisin, Herbert A.

doi:10.1117/12.723756

Cited by 5 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section we illustrate the potential usefulness of the MPSM for clustering and classification by showing results on synthetic data that closely model the form of real signals [14]. Each x i ∈ 101 in the training set has a binary label y j associated with it.…”

Section: Resultsmentioning

confidence: 99%

A matching pursuit based similarity measure for fuzzy clustering and classification of signals

Mazhar¹,

Gader

Wilson

2008

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)

Self Cite

View full text Add to dashboard Cite

Abstract-Matching pursuits is a well known technique for signal representation and has also been used as a feature extractor for some classification systems. However, applications that use matching pursuits (MP) algorithm in their feature extraction stage are quite problem domain specific, making their adaptation for other types of problems quite hard. In this paper we propose a matching pursuits based similarity measure that uses only the dictionary, coefficients and residual information provided by the MP algorithm while comparing two signals. Hence it is easily applicable to a variety of problems. We show that using the MP based similarity measure for competitive agglomerative fuzzy clustering leads to an interesting and novel update equation that combines the standard fuzzy prototype updating equation with a term involving the error between approximated signals and approximated prototypes. The potential value of the similarity measure is investigated using the fuzzy K-nearest prototype algorithm of Frigui for a two-class, signal classification problem. It is shown that the new similarity measure significantly outperforms the Euclidean distance.

show abstract

Section: Resultsmentioning

confidence: 99%

A matching pursuit based similarity measure for fuzzy clustering and classification of signals

Mazhar¹,

Gader

Wilson

2008

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similar feature-engineering examples can be found for handheld BEH detectors as well. [9][10][11][12] As in most pattern recognition applications, including detection and discrimination of BEHs using GPR, much of the effort in developing pattern recognition algorithms goes into the design of feature extraction, preprocessing and data transformations.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural networks for classifying GPR B-scans

Besaw

Stimac

2015

SPIE Proceedings

View full text Add to dashboard Cite

Symmetric and asymmetric buried explosive hazards (BEHs) present real, persistent, deadly threats on the modern battlefield. Current approaches to mitigate these threats rely on highly trained operatives to reliably detect BEHs with reasonable false alarm rates using handheld Ground Penetrating Radar (GPR) and metal detectors. As computers become smaller, faster and more efficient, there exists greater potential for automated threat detection based on state-of-the-art machine learning approaches, reducing the burden on the field operatives. Recent advancements in machine learning, specifically deep learning artificial neural networks, have led to significantly improved performance in pattern recognition tasks, such as object classification in digital images. Deep convolutional neural networks (CNNs) are used in this work to extract meaningful signatures from 2-dimensional (2-D) GPR B-scans and classify threats. The CNNs skip the traditional "feature engineering" step often associated with machine learning, and instead learn the feature representations directly from the 2-D data. A multi-antennae, handheld GPR with centimeter-accurate positioning data was used to collect shallow subsurface data over prepared lanes containing a wide range of BEHs. Several heuristics were used to prevent over-training, including cross validation, network weight regularization, and "dropout." Our results show that CNNs can extract meaningful features and accurately classify complex signatures contained in GPR B-scans, complementing existing GPR feature extraction and classification techniques.

show abstract

Deep learning algorithms for detecting explosive hazards in ground penetrating radar data

Besaw

Stimac

2014

SPIE Proceedings

View full text Add to dashboard Cite

Development of region processing algorithm for HSTAMIDS: status and field test results

Cited by 5 publications

References 5 publications

A matching pursuit based similarity measure for fuzzy clustering and classification of signals

A matching pursuit based similarity measure for fuzzy clustering and classification of signals

Deep convolutional neural networks for classifying GPR B-scans

Deep learning algorithms for detecting explosive hazards in ground penetrating radar data

Contact Info

Product

Resources

About